Xianchao Qiu

Toward Accurate and Fast Iris Segmentation for Iris Biometrics

Iris segmentation is an essential module in iris recognition because it defines the effective image region used for subsequent processing such as feature extraction. Traditional iris segmentation methods often involve an exhaustive search of a large parameter space, which is time consuming and sensitive to noise. To address these problems, this paper presents a novel algorithm for accurate and fast iris segmentation. After efficient reflection removal, an Adaboost-cascade iris detector is first built to extract a rough position of the iris center. Edge points of iris boundaries are then detected, and an elastic model named pulling and pushing is established. Under this model, the center and radius of the circular iris boundaries are iteratively refined in a way driven by the restoring forces of Hookes law. Furthermore, a smoothing spline-based edge fitting scheme is presented to deal with noncircular iris boundaries. After that, eyelids are localized via edge detection followed by curve fitting. The novelty here is the adoption of a rank filter for noise elimination and a histogram filter for tackling the shape irregularity of eyelids. Finally, eyelashes and shadows are detected via a learned prediction model. This model provides an adaptive threshold for eyelash and shadow detection by analyzing the intensity distributions of different iris regions. Experimental results on three challenging iris image databases demonstrate that the proposed algorithm outperforms state-of-the-art methods in both accuracy and speed.

Counterfeit iris detection based on texture analysis

This paper addresses the issue of counterfeit iris detection, which is a liveness detection problem in biometrics. Fake iris mentioned here refers to iris wearing color contact lens with textures printed onto them. We propose three measures to detect fake iris: measuring iris edge sharpness, applying Iris-Texton feature for characterizing the visual primitives of iris textures and using selected features based on co-occurrence matrix (CM). Extensive testing is carried out on two datasets containing different types of contact lens with totally 640 fake iris images, which demonstrates that Iris-Texton and CM features are effective and robust in anticounterfeit iris. Detailed comparisons with two state-of-the-art methods are also presented, showing that the proposed iris edge sharpness measure acquires a comparable performance with these two methods, while Iris-Texton and CM features outperform the state-of-the-art.

Boosting ordinal features for accurate and fast iris recognition

In this paper, we present a novel iris recognition method based on learned ordinal features.Firstly, taking full advantages of the properties of iris textures, a new iris representation method based on regional ordinal measure encoding is presented, which provides an over-complete iris feature set for learning. Secondly, a novel Similarity Oriented Boosting (SOBoost) algorithm is proposed to train an efficient and stable classifier with a small set of features. Compared with Adaboost, SOBoost is advantageous in that it operates on similarity oriented training samples, and therefore provides a better way for boosting strong classifiers. Finally, the well-known cascade architecture is adopted to reorganize the learned SOBoost classifier into a dasiacascadepsila, by which the searching ability of iris recognition towards large-scale deployments is greatly enhanced. Extensive experiments on two challenging iris image databases demonstrate that the proposed method achieves state-of-the-art iris recognition accuracy and speed. In addition, SOBoost outperforms Adaboost (Gentle-Adaboost, JS-Adaboost, etc.) in terms of both accuracy and generalization capability across different iris databases.

Robust eyelid, eyelash and shadow localization for iris recognition

Eyelids, eyelashes and shadows are three major challenges for effective iris segmentation, which have not been adequately addressed in the current literature. In this paper, we present a novel method to localize each of them. First, a novel coarse-line to fine-parabola eyelid fitting scheme is developed for accurate and fast eyelid localization. Then, a smart prediction model is established to determine an appropriate threshold for eyelash and shadow detection. Experimental results on the challenging CASIA-IrisV3-Lamp iris image database demonstrate that the proposed method outperforms state-of-the-art methods in both accuracy and speed.

Coarse Iris classification by learned visual dictionary

In state-of-the-art iris recognition systems, the input iris image has to be compared with a large number of templates in database. When the scale of iris database increases, they are much less efficient and accurate. In this paper, we propose a novel iris classification method to attack this problem in iris recognition systems. Firstly, we learned a small finite dictionary of visual words(clusters in the feature space), which are called Iris-Textons, to represent visual primitives of iris images. Then the Iris-Texton histograms are used to represent the global features of iris textures. Finally, K-means algorithm is used for classifying iris images into five categories. Based on the proposed method, the correct classification rate is 95% in a five-category iris database. By combining this method with traditional iris recognition algorithm, our system shows better performance in terms of both speed and accuracy.

Gesture recognition using temporal template based trajectories

In this paper, a novel approach of hand gesture recognition is proposed. The spatial-temporal trajectory of hand gesture is first tracked by the mean shift embedded particle filter (MSEPF), and then represented in a static image using temporal template. Hand gestures are recognized by a two-layer classifier, which is based on statistical shape and orientation analysis of such temporal template based trajectories (TTBT). Experimental results show that our algorithm has high recognition rate.

Learning Appearance Primitives of Iris Images for Ethnic Classification

Iris pattern is commonly regarded as a kind of phenotypic feature without relation to genes. In our previous work, we argued that iris texture is race related, and its genetic information is illustrated in coarse scale texture features, rather than preserved in the minute local features of state-of-the-art iris recognition algorithms. In this paper, we propose a novel ethnic classification method based on learning appearance primitives of iris images. So we not only confirm that iris texture is race related, but also try to find out which kinds of iris visual primitives make iris images look different between Asian and non-Asian. In our scheme, we learned a small finite vocabulary of micro-structures, which are called iris-textons, to represent visual primitives of iris images. Then we use iris-texton histogram to capture the difference between iris textures. Finally iris images are grouped into two race categories, Asian and non-Asian, by support vector machine (SVM). Based on the proposed method, we get a higher correct classification rate (CCR) of 91.02% than our previous method on a database containing 2400 iris samples.

Self-adaptive iris image acquisition system

Iris image acquisition is the fundamental step of the iris recognition, but capturing high-resolution iris images in real-time is very difficult. The most common systems have small capture volume and demand users to fully cooperate with machines, which has become the bottleneck of iris recognitions application. In this paper, we aim at building an active iris image acquiring system which is self-adaptive to users. Two low resolution cameras are co-located in a pan-tilt-unit (PTU), for face and iris image acquisition respectively. Once the face camera detects face region in real-time video, the system controls the PTU to move towards the eye region and automatically zooms, until the iris camera captures an clear iris image for recognition. Compared with other similar works, our contribution is that we use low-resolution cameras, which can transmit image data much faster and are much cheaper than the high-resolution cameras. In the system, we use Haar-like cascaded feature to detect faces and eyes, linear transformation to predict the iris cameras position, and simple heuristic PTU control method to track eyes. A prototype device has been established, and experiments show that our system can automatically capture high-quality iris image in the range of 0.6m×0.4m×0.4m in average 3 to 5 seconds.

Enhanced usability of iris recognition via efficient user interface and iris image restoration

In this paper, we investigate the possibility of enhancing the usability of iris recognition via exploration of the specular spots in iris images. Firstly, the spatial configuration of the specular spots in iris images is utilized to estimate the distance between the user and the camera. Based on this a friendly user interface is established to assist users for their range adjustment. Furthermore, the estimated distance is used by an adaptive image restoration scheme to restore the blurred iris image, thereby increasing the depth of field of the iris camera. Experimental results show that the proposed method significantly enhances the usability of iris recognition without noticeable computation cost.